This study focuses on evaluating the performance of three large language models - Llama2-7B, GPT-3.5, and Falcon-7B - in the task of domain-driven keyword extraction. The researchers utilized the Inspec and PubMed datasets, which represent scientific literature from various disciplines, to assess the models' capabilities.
The study employed the Jaccard similarity index to quantitatively compare the keywords extracted by the models against the reference keywords from the datasets. The results showed that GPT-3.5 achieved the highest average Jaccard similarity scores of 0.64 for the Inspec dataset and 0.21 for the PubMed dataset, indicating a substantial overlap between its generated keywords and the reference sets.
Llama2-7B demonstrated a more mixed performance, with a Jaccard similarity score of 0.40 for Inspec and 0.17 for PubMed. The researchers noted that Llama2-7B exhibited a tendency to generate additional keywords that were not present in the reference sets, suggesting its ability to identify potentially relevant terms that may have been overlooked. However, this also contributed to a lower overall Jaccard similarity score, particularly for the PubMed dataset.
Falcon-7B showed the lowest Jaccard similarity scores, with 0.23 for Inspec and 0.12 for PubMed, indicating a limited overlap between its generated keywords and the reference sets. The researchers observed that Falcon-7B's output included unnecessary words, which negatively impacted the overall quality of the extracted keywords.
The study also discussed the impact of hallucination, a phenomenon where LLMs generate factually inaccurate information, on the evaluation and interpretation of the results. The researchers highlighted the need to understand and address these hallucination-related challenges to optimize the performance of LLMs in domain-specific keyword extraction tasks.
Furthermore, the study emphasized the importance of prompt engineering techniques in guiding the LLMs towards more effective and accurate keyword extraction. The researchers developed a custom Python package that seamlessly integrates with the LangChain framework, enabling efficient interaction with the various LLMs and facilitating the application of prompt engineering strategies.
Overall, this study provides valuable insights into the comparative performance of Llama2-7B, GPT-3.5, and Falcon-7B in domain-specific keyword extraction tasks, highlighting the strengths and limitations of each model. The findings contribute to the ongoing research and development in the field of natural language processing, particularly in the context of leveraging large language models for data enrichment and content analysis.
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by Sandeep Chat... at arxiv.org 04-04-2024
https://arxiv.org/pdf/2404.02330.pdfDeeper Inquiries